How do the characteristics of breast cancer diagnostic assessment programmes influence service delivery: A mixed methods study
Bibliographic record
Abstract
Diagnostic assessment programmes (DAPs) coordinate multidisciplinary teamwork (MDT), and improve wait times and patient satisfaction. No research has established optimal DAP design. This study explored how DAP characteristics influence service delivery. A mixed methods case study of four breast cancer DAPs was conducted including qualitative interviews with health-care providers and retrospective chart review. Data were integrated using multiple approaches. Twenty-three providers were interviewed; 411 medical records were reviewed. The number of visits and wait times from referral to diagnosis and consultation were lowest at a one-stop model. DAP characteristics (rural-remote region, human resources, referral volume, organisation of services, adherence to service delivery targets and one-stop model) may influence service delivery (number of visits, wait times). MDT, influenced by other DAP characteristics (co-location of staff, patient navigators, team functioning), may also influence service delivery. While the one-stop model may be ideal, all sites experienced similar and unique challenges. Further research is needed to understand how to optimise the organisation and delivery of DAP services. Measures reflecting individual, team and patient-reported outcomes should be used to assess the effectiveness and impact of DAPs in addition to more traditional measures such as wait times.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".